- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
Here are some of the projects that our MPS students have completed in the past, including the client, participating student/s, the semester in which it was conducted and a project description. If you are a company interested in proposing an MPS project, please email the MPS project instructor at firstname.lastname@example.org.
Content and Layout Editing Redesign and Test (Spring 2018)
- Client: Drupal
- Team Members: Zhengnan Zhoa, Stella Huang, Yifan Yao, and Vivian Jiang
- Drupal, a content management system that powers more than a million websites, currently does not have a layout builder as part of Drupal core. In this project, the team redesigned a feature to allow users to visually construct the layout and contents of a page.
Android Mobile App for WiFi Visualization (Spring 2018)
- Client: MITRE
- Team Members: Yoo Kim, Shuran Li, Guanyu Chen, and Taira Davey
- This team aimed to design and develop an Android mobile app that displays RF signals, such as WiFi, through a data visualization that is informative, intuitive, and unique. The app displays the strongest Bluetooth and WiFi sources in a given area, along with detail information about each source and graphs to show signal strength over time.
Private Eye (Spring 2018)
- Client: Microsoft Research
- Team Members: Zhili Huang, Aruhan Liu, and Zhaoxing Wu
- A maze solver coding game, Private Eye challenges players to construct single pieces of code that, when done correctly, guides the main character, Private Eye, through a series of mazes. The team chose to create Private Eye because maze-solving is inherently a problem with a programmatic solution. The idea to use the same piece of code to defeat all levels was inspired by units tests in programming.
Design System (Spring 2018)
- Client: Assurant
- Team Members: Bingxin Weng, Eric Gendreau, and Hanchun Shao
- This team researched and built a design system to increase efficiency at Assurant. Reviewing existing design systems from industry leaders, researching best practices and holding in-person interviews with Assurant stakeholders, the trio's Assurant Design System combines intuitive UX components with the unique needs of Assurant for a system poised to increase productivity and provide engaging experiences for the company's customers.
KAMEN: Programmable Augmented Reality (Fall 2017)
- Client: Microsoft Research
- Team Members: Di Chen, Chun Jiang, and Yanfei Yu
- Empowered with MakeCode – the open sourcing platform to leverage computing education – our team built KAMEN to create an interactive project that fully integrates MakeCode's educational functions. The goal was to help users build a basic programming mindset and, in the process, enable them to better understand adding an augmented reality view to an existing web application.
Diabetes Self-Management via Facebook Messenger (Fall 2017)
- Client: Epharmix
- Team Members: Youming Luo, Haotian Li, and Daniel Yoon
- Epharmix's current product offering relies on SMS and phone call-based surveys and is thus limited by the capabilities of these two channels. Our team explored a Facebook Messenger chat bot that helps users better manage their own health by providing condition-specific information, notifications, and other digital interventions.
Machine Learning Approach to Modeling User Behavior (Fall 2017)
- Client: Pendo
- Team Members: Siyuan Yin, Daniel Lee, and Alex Cho
- Pendo.io helps companies measure customer experience by capturing user behavior, analyzing features that drive engagement, building reports, and more. Our team's goal was to analyze historical product data to build models using machine learning algorithms in order to better understand Pendo's customers.
United Insights (Spring 2017)
- Client: Credit Suisse
- Team Members: Lini Tan, Hsuan Huang, and Dongdong Yu
- Working for a financial and investment services holding company requires you to deal with lots of tasks every day. Shifting between email, Skype, and messengers can be tedious and inefficient. You need a better platform that integrates your tasks, collaborators, and tools. Our team focused on designing key use-cases around client view of this collaboration workspace.
Evaluation of Google Cloud Machine Learning Platform (Spring 2017)
- Client: Google
- Team members: Tony Fan, Ningxuan He, Anagha Todalbagi, Sri Tapasya Kothapally
- This student team evaluated the usability of the Google Machine Learning Platform by analyzing two data sets. First, the team looked at breast cancer data and built a model to predict metastasis of breast cancer. Next, they used Vision API to analyze dog breed image analytics.
UX for Search Engine Trustworthiness (Spring 2016)
- Client: Yahoo!
- Team members: Mobile - Kramer Canfield, Xiangyi Li, Chenyu Li; Desktop - Trinh Le, Jing Zhang, Yeon Soo Park
- With the rising popularity and use of smartphones, mobile search continues to grow and more kinds of search become context-aware. In order to explore the trustworthiness of mobile search results, the team conducted an exploratory research project for our client Yahoo! to see if they could reveal how users think about search and why they trust certain results over others. The team conducted an online survey and individual user interviews in which they asked participants to complete mobile search tasks and explain their thought process along the way. The collective findings revealed that users place more trust in popular websites and those they are familiar with than those that they have not visited before. They also observed a number of interaction difficulties relating to efficiency, display of information, and choice of application. From their observations, they developed four new feature designs to address these findings and help users have a better user experience in order to increase satisfaction and the perceived trustworthiness of search results.
Intelligent Financial Assistant (Spring 2016)
- Client: S&P Capital and SNL
- Team Members: Ruchir Hajela, Jingchen Li, Yangwen Wan, Shibo Zang
- From team member Jingchen Li: "The team built a voice assistant for the company’s app and is currently building an Xcode prototype on speech-to-text. The next step is to test MS Project Oxford and Wit.ly’s NLP APIs and build an app using Cordova."